BIDS’ Tim Thomas (Research Training Lead for Berkeley Computational Social Science Program) and his team at Berkeley’s Urban Displacement Project (UDP) have just released a first-of-its-kind machine learning model that predicts the risk of low-income renter displacement at the neighborhood level. With over a year of development, the Estimated Displacement Risk (EDR) Model uses a decade’s worth of household level data, controlling for over 600 census, market, housing, and demographic variables to predict which neighborhoods are at the greatest risk of displacement.
For this initial launch, the UDP has applied the EDR to the state of California, which is helping local and state agencies understand where displacement risk is highest, and planning is under way to continue expanding the project to conduct analyses across the rest of the country by early 2023. Throughout, Thomas plans to link unique eviction and employment data to the EDR in order to improve the UDP’s Housing Precarity Risk Model, which helps local, state, and federal agencies understand drivers of vulnerability and recommend policies to deter displacement.
Read more about the California Estimated Displacement Risk Model (Authors: Tim Thomas, Karen Chapple, Julia Greenberg, Alex Ramiller, Emery Reifsnyder, Isaac Schmidt, Kate Ham (June 20, 2022).